After going through the fundamentals of the TSB Method for Intermittent Time Series Forecasting in NumPyro in the previous notebook, we explore a variation of it that might be useful for certain applications. In a nutshell, we keep the same model structure of the TSB model, but we modify the likelihood function to account for the sparsity of the time series. Concretely, we replace the classic Gaussian likelihood function with a zero-inflated Negative Binomial likelihood function. One clear conceptual advantage of this approach that all our prediction and credible intervals will be non-negative.
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After going through the fundamentals of the TSB Method for Intermittent Time Series Forecasting in NumPyro in the previous notebook, we explore a variation of it that might be useful for certain applications. In a nutshell, we keep the same model structure of the TSB model, but we modify the likelihood function to account for the sparsity of the time series. Concretely, we replace the classic…
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